MACHINE LEARNING

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Resultados 219 results. LastUpdate Updated on 18/06/2021 [20:37:00] pdf PDF xls XLS

Solicitudes publicadas en los últimos 60 días / Applications published in the last 60 days



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DEEP FUSION REASONING ENGINE (DFRE) FOR PRIORITIZING NETWORK MONITORING ALERTS

Publication No.: US2021184915A1 17/06/2021

Applicant:

CISCO TECH INC [US]

US_2019306011_PA

Absstract of: US2021184915A1

In one embodiment, a service that monitors a network detects a plurality of anomalies in the network. The service uses data regarding the detected anomalies as input to one or more machine learning models. The service maps, using a conceptual space, outputs of the one or more machine learning models to symbols. The service applies a symbolic reasoning engine to the symbols, to rank the anomalies. The service sends an alert for a particular one of the detected anomalies to a user interface, based on its corresponding rank.

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VIRTUAL DATA SCIENTIST WITH PRESCRIPTIVE ANALYTICS

Publication No.: US2021182701A1 17/06/2021

Applicant:

ACCENTURE GLOBAL SOLUTIONS LTD [IE]

Absstract of: US2021182701A1

A data analytics platform may determine whether a machine learning model is a regression model. The data analytics platform may perform, based on determining that the machine learning model is a regression model, a regression prescription method including acquiring a predicted value of a performance indicator determined by the machine learning model processing data associated with a plurality of features and the performance indicator, acquiring a target value of the performance indicator, determining a rate of change of the performance indicator with respect to each feature to generate first results, determining, based on the regression model and for each feature, a rate of change of each feature with respect to other features to generate second results, and determining, for each feature and based on the predicted value, the target value, the first results, and the second results, a change in each feature to achieve the target value.

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SYSTEMS AND METHODS FOR DETERMINING RELATIVE IMPORTANCE OF ONE OR MORE VARIABLES IN A NON-PARAMETRIC MACHINE LEARNING MODEL

Publication No.: US2021182706A1 17/06/2021

Applicant:

CAPITAL ONE SERVICES LLC [US]

US_10832147_B1

Absstract of: US2021182706A1

Systems and methods for determining relative importance of one or more variables in a non-parametric model include: receiving, raw values of the variables corresponding to one or more entities; processing the raw values using a statistical model to obtain probability values for the variables and an overall prediction value for each entity; determining a plurality of cumulative distributions for the variables based on the raw values and the number of entities having a specific raw value; grouping the variables into a plurality of equally sized buckets based on the cumulative distributions; determining a mean probability value for each bucket; assigning a rank number for each bucket based on the mean probability values; compiling a table for the entities based on the raw values and the buckets corresponding to the raw values; and determining the relative importance of the variables for the entities based on the rank numbers.

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METHOD AND SYSTEM FOR REMOTELY MONITORING THE PSYCHOLOGICAL STATE OF AN APPLICATION USER USING MACHINE LEARNING-BASED MODELS

Publication No.: US2021177324A1 17/06/2021

Applicant:

MAHANA THERAPEUTICS INC [US]

Absstract of: US2021177324A1

An application user is granted access to one or more applications that provide the user with information and assistance. Through the one or more applications, the user is provided with interactive content, and data related to aspects of the user's interaction with the provided content is collected. The collected interaction data is analyzed to remotely identify and monitor changes or anomalies in the psychological state of the user using machine learning-based models. Upon identification of changes or anomalies in the user's psychological state, one or more actions are taken to assist the user.

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ADDRESSABLE SMART AGENT DATA TECHNOLOGY TO DETECT UNAUTHORIZED TRANSACTION ACTIVITY

Publication No.: US2021182766A1 17/06/2021

Applicant:

BRIGHTERION INC [US]

US_2018130006_PA

Absstract of: US2021182766A1

A computer implemented and electronic process is provided that uses artificial intelligence to detect unauthorized activity by an insider or hacker. Electronic systems that employ artificial intelligence and machine learning to detect unauthorized transaction activity by insiders or hackers for a computer network system are also provided. Hardware required for carrying out the invention typically include a plurality of networked computers. Specialized software and/or firmware is typically needed in connection with the hardware for carrying out the invention.

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AUTOMATED RULE GENERATION FRAMEWORK USING MACHINE LEARNING FOR CLASSIFICATION PROBLEMS

Publication No.: EP3836036A1 16/06/2021

Applicant:

SAP SE [DE]

US_2021133515_A1

Absstract of: EP3836036A1

Methods, systems, and computer-readable storage media for receiving historical data, the historical data including variable vectors, each variable vector being assigned to a class, processing the historical data through encoders to provide feature vectors, each feature vector corresponding to a respective variable vector and being assigned to the class of the respective variable vector, generating a set of decision trees based on the feature vectors, each decision tree corresponding to a class in the set of classes, transforming each decision tree into a set of rules to provide sets of rules, each rule in a set of rules defining conditions to assign at least a portion of an electronic document to a respective class in the set of classes, and providing the sets of rules for execution in an enterprise system, the enterprise system classifying electronic documents to classes in the set of classes based on the sets of rules.

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METHOD AND ELECTRONIC DEVICE FOR ACCIDENTAL TOUCH PREDICTION USING ML CLASSIFICATION

Publication No.: EP3835929A1 16/06/2021

Applicant:

SAMSUNG ELECTRONICS CO LTD [KR]

Absstract of: EP3835929A1

A method for accidental touch prediction using machine learning (ML) classification is provided. The method includes determining, by an electronic device, a mutual data index of a sensor data using a first ML model. Further, the method includes recognizing whether the sensor data corresponds to an object touch or a non-object touch based on the mutual data index. Further, the method includes performing, by the electronic device, one of detecting that the electronic device is in a pocket mode and providing an object touch notification on a touch screen of the electronic device in response to determining that the sensor data corresponds to the object touch, and recognizing whether the sensor data corresponds to an accidental touch or a non-accidental touch using at least one second ML model based on touch data in response to determining that the sensor data corresponds to the non-object touch.

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MODEL GENERATION DEVICE, MODEL GENERATION METHOD, AND NON-TRANSITORY RECODING MEDIUM

Publication No.: US2021174231A1 10/06/2021

Applicant:

NEC CORP [JP]

WO_2020026395_PA

Absstract of: US2021174231A1

Disclosed is a model generation device capable of mitigating the risk of overlooking a phenomenon of interest in machine learning. The model generation device determines whether or not a label of a first data is similar to a label of a second data. The model generation device assigns the label of the second data to the first data when determining that the label of the first data is similar to the label of the second data based on a degree of similarity between observation information representing a state where the first data is observed and observation information representing a state where the second data is observed. The model generation device calculates model representing a relevance between data information containing the first data and the second data and label information containing the assigned label and the label of the second data.

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USING DEEP LEARNING TO DETERMINE GAZE

Publication No.: WO2021113100A1 10/06/2021

Applicant:

FACEBOOK TECH LLC [US]

US_2021174589_A1

Absstract of: WO2021113100A1

In one embodiment, a computing system may generate and display a virtual reality environment to a user. The computing system may determine a head pose of the user based on headset tracking data associated with a headset worn by the user. The computing system may determine a hand pose of the user based on hand tracking data associated with a device held or worn by a hand of the user. The computing system may access scene information associated with the displayed virtual reality environment. The computing system may determine a predicted focal point of the user within the virtual reality environment by processing the head pose, the hand pose, and the scene information using a machine-learning model.

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MACHINE LEARNING APPARATUS, CONTROL DEVICE, MACHINING SYSTEM, AND MACHINE LEARNING METHOD FOR LEARNING CORRECTION AMOUNT OF WORKPIECE MODEL

Publication No.: US2021174227A1 10/06/2021

Applicant:

FANUC CORP [JP]

JP_2021092954_A

Absstract of: US2021174227A1

A machine learning apparatus capable of reducing an error between a machined workpiece and a target shape when the workpiece is machined based on a workpiece model modeling the target shape of the workpiece. A machine learning apparatus includes a state observation section configured to observe machining state data of a machine tool configured to machine the workpiece, and measurement data of an error between a shape of the workpiece machined by the machine tool based on the workpiece model and the target shape, as a state variable representing a current state of environment in which the workpiece is machined, and a learning section configured to learn the correction amount in association with the error by using the state variable.

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METHOD AND APPARATUS FOR IMPLEMENTING A ROLE-BASED ACCESS CONTROL CLUSTERING MACHINE LEARNING MODEL EXECUTION MODULE

Publication No.: US2021174305A1 10/06/2021

Applicant:

JPMORGAN CHASE BANK NA [US]

Absstract of: US2021174305A1

Various methods, systems, apparatuses, and media for implementing a machine learning model execution module are provided. A processor accesses human resource (HR) attributes and profile information data of users from a database. The processor applies hierarchical clustering algorithm to create a machine learning model by clustering users based on accesses to applications that the users have corresponding to the profile information data of the users. All users in one cluster have the most similar accesses to applications. The processor iterates the process of accessing the HR attributes and the profile information data of the users from the database until it is determined that an optimal number of clusters have been created for the machine learning model.

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BIAS SCORING OF MACHINE LEARNING PROJECT DATA

Publication No.: US2021174222A1 10/06/2021

Applicant:

AT & T IP I LP [US]

Absstract of: US2021174222A1

Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.

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RECOMMENDATION ENGINE FOR RESOURCE TAGGING

Publication No.: US2021176191A1 10/06/2021

Applicant:

MICRO FOCUS LLC [US]

Absstract of: US2021176191A1

A resource recommendation system is described to recommend and standardize resource tagging in a networked computing environment. In one example, cloud resources and related data are discovered, a database of the discovered information is generated, machine learning is applied to the database to build a prediction model, and tags for the resources are recommended, based on the prediction model, at a computing device.

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MACHINE LEARNING INFERENCE CALLS FOR DATABASE QUERY PROCESSING

Publication No.: US2021174238A1 10/06/2021

Applicant:

AMAZON TECH INC [US]

WO_2021055478_A1

Absstract of: US2021174238A1

Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

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INTELLIGENT SERVICES FOR APPLICATION DEPENDENCY DISCOVERY, REPORTING, AND MANAGEMENT TOOL

Publication No.: US2021173763A1 10/06/2021

Applicant:

CAPITAL ONE SERVICES LLC [US]

US_2020409826_A1

Absstract of: US2021173763A1

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

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NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING ASSISTED CATALOGING AND RECOMMENDATION ENGINE

Publication No.: US2021174221A1 10/06/2021

Applicant:

PAYPAL INC [US]

Absstract of: US2021174221A1

Systems and methods that determining a solution for a real-time message are provided. Multiple messages of different types are received from multiple platforms. The messages were generated in response to errors caused by applications monitored by the platforms. For each message, a language processing system determines the content of the message and the machine learning system determines a classification of the message. The set of message candidates are generated by comparing the classification and the content of the message to historical messages. From the set of message candidates, solution messages are identified. A recommended solution is determined from the solution messages.

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BUILDING DEEP LEARNING ENSEMBLES WITH DIVERSE TARGETS

Publication No.: US2021174265A1 10/06/2021

Applicant:

D5AI LLC [US]

WO_2020036847_PA

Absstract of: US2021174265A1

A computer-implemented method of training an ensemble machine learning system comprising a plurality of ensemble members. The method includes selecting a shared objective and an objective for each of the ensemble members. The method further includes training each of the ensemble members according to each objective on a training data set, connecting an output of each of the ensemble members to a joint optimization machine learning system to form a consolidated machine learning system, and training the consolidated machine learning system according to the shared objective and the objective for each of the ensemble members on the training data set. The ensemble members can be the same or different types of machine learning systems. Further, the joint optimization machine learning system can be the same or a different type of machine learning system than the ensemble members.

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MACHINE LEARNING MONITORING SYSTEMS AND METHODS

Publication No.: US2021174258A1 10/06/2021

Applicant:

ARTHUR AI INC [US]

Absstract of: US2021174258A1

A method for monitoring performance of a ML system includes receiving a data stream via a processor and generating a first plurality of metrics based on the data stream. The processor also generates input data based on the data stream, and sends the input data to a machine learning (ML) model for generation of intermediate output and model output based on the input data. The processor also generates a second plurality of metrics based on the intermediate output, and a third plurality of metrics based on the model output. An alert is generated based on at least one of the first plurality of metrics, the second plurality of metrics, or the third plurality of metrics, and a signal representing the alert is sent for display to a user via an interface.

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Federated machine-Learning platform leveraging engineered features based on statistical tests

Publication No.: US2021174257A1 10/06/2021

Applicant:

CEREBRI AI INC [US]

Absstract of: US2021174257A1

Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.

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TRAINING TREE-BASED MACHINE-LEARNING MODELING ALGORITHMS FOR PREDICTING OUTPUTS AND GENERATING EXPLANATORY DATA

Publication No.: US2021174264A1 10/06/2021

Applicant:

EQUIFAX INC [US]

US_2020387832_A1

Absstract of: US2021174264A1

Certain aspects involve training tree-based machine-learning models for computing predicted responses and generating explanatory data for the models. For example, independent variables having relationships with a response variable are identified. Each independent variable corresponds to an action or observation for an entity. The response variable has outcome values associated with the entity. Splitting rules are used to generate the tree-based model, which includes decision trees for determining relationships between independent variables and a predicted response associated with the response variable. The tree-based model is iteratively adjusted to enforce monotonicity with respect to representative response values of the terminal nodes. For instance, one or more decision trees are adjusted such that one or more representative response values are modified and a monotonic relationship exists between each independent variable and the response variable. The adjusted model is used to output explanatory data indicating relationships between independent variable changes and response variable changes.

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ARTIFICIAL INTELLIGENCE DECISION MODELING PROCESSES USING ANALYTICS AND DATA SHAPELY FOR MULTIPLE STAKEHOLDERS

Publication No.: US2021174448A1 10/06/2021

Applicant:

KOTARINOS MICHAEL WILLIAM [US]
TSOKOS CHRISTOS [US]

Absstract of: US2021174448A1

Data Shapley is an approach to understand the role of data in a decision-making process. The present invention involves a process to connect Data Shapley to a data analytics and machine learning based decision-making environment through the use of utility functions. In the present invention a problem is structurally analyzed using machine learning and data analytics to determine structural trends. Data is then analyzed using Data Shapley to determine what additional information is needed to make a decision. This allows for the relevant data to be collected to estimate utility functions for participants. Data Shapley is then used again to decompose the decision-making process and look for trends in the process, and machine learning is applied to see if there are commonalities across the criteria in the decision-making process. After this, the decision-making process selects a strategy as the decision. If new information becomes available or an event occurs that makes a change of strategy necessary, then Data Shapley is used to guide the data acquisition and decision-making process. If no new information is available or an event does not occur, event occurrence is dynamically predicted using data analytics and Data Shapley proactively recommends what data streams to monitor and collect.

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NEGOTIATING MACHINE LEARNING MODEL INPUT FEATURES BASED ON COST IN CONSTRAINED NETWORKS

Publication No.: US2021176146A1 10/06/2021

Applicant:

CISCO TECH INC [US]

Absstract of: US2021176146A1

In one embodiment, a service receives a feature availability report indicative of which telemetry variables are available at a device in a network and resource costs associated with data features that the device could compute from the telemetry variables. The service selects at least a subset of the data features for input to a machine learning model, based on their associated resource costs and on their respective impacts on one or more performance metrics for the machine learning model. The service trains the machine learning model to evaluate the selected data features. The service sends the trained machine learning model to the device. The device computes the selected data features from the telemetry variables available at the device and uses the computed data features as input to the machine learning model.

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SCHEMA CORRESPONDENCE RULE GENERATION USING MACHINE LEARNING

Publication No.: US2021174219A1 10/06/2021

Applicant:

PEERNOVA INC [US]

Absstract of: US2021174219A1

A schema matching system processes training event data received from multiple sources to determine correspondence rules associating fields in the schemas of each source. To generate the correspondence rules, the schema matching system can use training event data from multiple sources comprising events associated with multiple schemas. Then, based on one or more similarity metrics between data entries of the training event data, the system matches individual events using a machine learning algorithm and, based on the pairs of matching events, corresponding schema fields across the multiple schemas. Based on the matching events and/or user feedback, the schema matching system can generate one or more correspondence rules based on the normalization rules and the corresponding fields of the schemas for later use by one or more transaction monitoring systems on incoming event data.

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Machine learning approach to real-time patient motion monitoring

Publication No.: AU2019366442A1 10/06/2021

Applicant:

ELEKTA INC

US_2020129780_PA

Absstract of: AU2019366442A1

Systems and techniques may be used to estimate a patient state during a radiotherapy treatment. For example, a method may include generating a dictionary of expanded potential patient measurements and corresponding potential patient states using a preliminary motion model. The method may include training, using a machine learning technique, a correspondence motion model relating an input patient measurement to an output patient state using the dictionary. The method may include estimating, using a processor, the patient state corresponding to an input image using the correspondence motion model.

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Next-generation molecular profiling

Nº publicación: AU2019389175A1 10/06/2021

Applicant:

CARIS MPI INC

WO_2020113237_A1

Absstract of: AU2019389175A1

Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that strongly correlate with response of colorectal cancer patients to FOLFOX. Described herein are data structures, data processing, and machine learning models to predict effectiveness of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising administration of 5-fluorouracil/leucovorin combined with oxaliplatin (FOLFOX) or with irinotecan (FOLFIRI).

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